To alleviate the recent shortage problem of nurses, the actualization of RSN (Robotic Scrub Nurse) that can autonomously judge the current step of the surgery and pass the surgical instruments needed for the next step to surgeons is desired. The authors developed a computer vision based algorithm that can early-recognize only two steps of suture surgery. Based on the past work, this paper explores the effectiveness of utilizing temporal order of the six steps in suture surgery for the early-recognition. Our early-recognition algorithm consists of two modules: start point detection and hand action early-recognition. Segments of the test video that start from each quasi-start point are compared with the training data, and their probabilities are calculated. According to the calculated probabilities, hand actions could be early-recognized. To improve the early-recognition accuracy, temporal order information could be useful. This paper checks confusions of three steps' early recognition results, and if necessary, early-recognizes again after eliminating the wrong result, while for the other three steps, temporal order information is not utilized. Experimental results show our early-recognition method that utilizes the temporal order information achieves better performances.
|Title of host publication
|RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2017 Dec 8
|26th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2017 - Lisbon, Portugal
Duration: 2017 Aug 28 → 2017 Sept 1
|26th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2017
|17/8/28 → 17/9/1
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Human-Computer Interaction
- Control and Optimization